Risk stratification of immunocompromised children, including pediatric transplant recipients at risk of severe respiratory syncytial virus disease
Bibliographic record
Abstract
BACKGROUND: Respiratory syncytial virus (RSV) infection is associated with increased morbidity and mortality in immunocompromised patients. Our goal was to develop a framework for risk stratifying immunocompromised patients, including transplant patients, for RSV prophylaxis. METHODS: Risk factors for severe RSV disease in immunocompromised patients were identified in the literature and by an expert panel via survey. Experts assigned a probability of developing severe disease (0 to 100 scale) to the risk factors for each immunocompromised population. The results were validated using a clinical dataset. Linear mixed models adjusted for within-expert clustering of ranks were used to estimate average scores, and differences were tested using paired t tests. Logistic regression was utilized to identify important determinants of severe RSV disease. RESULTS: The survey was emailed to twenty-seven experts and thirteen responded (48%). Across all transplant groups, age <2 years (mean 77.1, 95% CI 71.7, 82.5) and day care attendance (mean 72.8, 95% CI 67.3, 78.3) were assigned the highest risk of severe disease. The highest risk groups were lung transplant recipients (mean 73.2, 95% CI 67.6, 78.8), combined lung and heart transplant recipients (mean 75.2, 95% CI 69.6, 80.7), allogeneic stem cell transplant (mean 76.0, 95% CI 70.4, 81.6), and severe combined immunodeficiency (mean 74.7, 95% CI 69.1, 80.3). CONCLUSION: The results provide a logical validity to current practice and provide guidance for prioritizing patients to receive prophylactic agents to prevent severe RSV disease. The results will facilitate the development of a risk stratification tool for RSV prophylaxis for immunocompromised patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".